Surveillance cameras are increasingly deployed in public places, monitoring the areas of interest to ensure security. With the stream of data from these video cameras, there has been a rise in video analysis and anomaly detection research. Anomalies are significant deviations of scene entities from normal behavior [1]}, [2]}. Fighting, traffic accidents, burglary, and robbery are a few examples of anomalies. Compared to normal activities, anomalous events rarely occur. Therefore, intelligent computer vision algorithms are required to detect anomalous events automatically, to avoid the waste of time and labor. In some methods, anomaly detection models are trained with normal behaviors to learn distributions of normal patterns. These models identify anomalous activities based on dissimilarity to the standard data distributions [3]}, [4]}. In other cases, normal and anomalous videos are used during training to automatically predict high anomaly scores [5]}, [6]}. In many real-time applications, the system must detect anomalous events as soon as each video frame arrives, only based on history and the current data; for instance, an intelligent video surveillance application designed to raise the alarm when suspicious activity is detected. To this end, online action detection algorithms are developed to accumulate historical observations and predicted future information to analyze current events [7]}, [8]}, [9]}, [10]},[11]}.
